Data Visualization Techniques

Content

• The grammar of graphics
• The major components of layers
• Hands on practice
• Visualizations based on the gg approach

The grammar of graphics

The grammar of graphics is about grammatical rules for creating perceivable graphs, or what we call graphics. (Leland Wilkinson, 2005).


Take the analogy: good grammar is just the first step in creating a good sentence.

Layers of the grammar of graphics

A layer is composed of

1. data and aesthetic mappings
2. a geometric object
3. a statistical transformation
4. a position adjustment

1. Data and aesthetic mapping

Scales — Mapping Data to Aesthetic Attributes

A scale defines how data values are translated into visual properties (aesthetics).

Each aesthetic — color, size, shape, position, etc. — has a corresponding scale.

You can customize scales to control:

    Color palettes
    Axis limits and breaks
    Legend appearance
    Transformations (e.g., log, sqrt)

Mapping data to aesthetics

• Left, continuous data to size and color
• Right, discrete data to shape and color

Aestetic mappings

2. Geometric objects

Geometric object

Aesthetics mapping in practice

library(dviz.supp)
library(forcats)
library(lubridate)

if (!requireNamespace("gt")) install.packages("gt")
library(gt)

Mapping and geometry

p <- ggplot(temps_long, 
            aes(x = date, 
                y = temperature, 
                color = location)
            ) +
  geom_line(linewidth = 1) +
  scale_x_date(name = "month", 
               limits = c(ymd("0000-01-01"), ymd("0001-01-04")),
               breaks = c(ymd("0000-01-01"), ymd("0000-04-01"), ymd("0000-07-01"),
                          ymd("0000-10-01"), ymd("0001-01-01")),
               labels = c("Jan", "Apr", "Jul", "Oct", "Jan"), expand = c(1/366, 0)) + 
  scale_y_continuous(limits = c(19.9, 107),
                     breaks = seq(20, 100, by = 20),
                     name = "temperature (°F)") +
  scale_color_OkabeIto(order = c(1:3, 7), name = NULL) +
  theme_dviz_grid() +
  theme(legend.title.align = 0.5)

Temperature plot

Seaborn and the Grammar of Graphics

# Create plot
fig, ax = plt.subplots(figsize=(9, 5))

# Use seaborn lineplot; pass palette by mapping
sns.lineplot(
    data=lf,
    x='date',
    y='temperature',
    hue='location',
    palette=palette_map,
    linewidth=1.5,  # similar to geom_line linewidth
    ax=ax
)

# X-axis limits and breaks (use valid years 2000-01-01 to 2001-01-04)
xmin = pd.to_datetime("2000-01-01")

Temperature plot using Seaborn

(np.float64(10957.0), np.float64(11326.0))

(19.9, 107.0)

Changing the geometry to heatmap

Preprocessing:

• Compute mean by location & month
• Replace month numbers with names

Aesthetics mapping and geometry

p <- ggplot(mean_temps, 
            aes(x = month, y = location, fill = mean)) + 
     geom_tile(width = .95, height = 0.95) +
     scale_fill_viridis_c(option = "B", begin = 0.15, end = 0.98,
                       name = "temperature (°F)") + 
     scale_y_discrete(name = NULL) +
     ...

3. Statistical transformations

Common statistical transformations

Contours

Plot the blue jay relationship between body mass and head length.

Contour plot, first version

blue_jays_base <- ggplot(blue_jays, aes(Mass, Head)) + 
  scale_x_continuous(limits = c(57, 82), expand = c(0, 0), name = "body mass (g)") +
  scale_y_continuous(limits = c(49, 61), expand = c(0, 0), name = "head length (mm)" ) +
  theme_dviz_grid()

blue_jays_base + 
  stat_density_2d(color = "black", size = 0.4, binwidth = 0.004) +
  geom_point(color = "black", size = 1.5, alpha = 1/3)

Apply some shading

blue_jays_base + 
  stat_density_2d(aes(fill = ..level..), geom = "polygon", color = "black", size = 0.15, binwidth = 0.004) +
  geom_point(color = "black", size = 1.5, alpha = .4) +
  scale_fill_gradient(low = "grey95", high = "grey70", guide = "none")

Grouping by sex

blue_jays_base + 
  aes(color = KnownSex) +
  stat_density_2d(size = 0.4, binwidth = 0.006) +
  geom_point(size = 1.5, alpha = 0.7) +
  ...

Bins

Common applications:

• Histograms
• Contours
• Heatmaps, aggregate values into grid cells to display intensity across two dimensions
• Temporal aggregation
• Large-data intensity approximation

Result

4. Position adjustment

Position Adjustments in ggplot2

Position dodge

Using jitter to deal with occlusion

Partial transparency

Jitter

Using jitter to visualize outliers

The facets layer

• Faceting = splitting a dataset into subsets and drawing the same plot design for each subset.
• It’s a declarative way to show conditional relationships: “plot y vs x for each level of variable z”.
• Benefits: compares patterns across groups while keeping scales and geoms consistent.
• The row and column variables must be categorical.

Violin plots example

Heatmap facets example

Plotly Express — interactive faceting

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", facet_col="sex", color="smoker",
                 title="Tip vs Bill faceted by sex")
fig.update_layout(legend_title_text="Smoker");
fig.show()

Coodinate systems

• In the Grammar of Graphics, the coordinate system defines how data coordinates are mapped to the 2D plane of the plot.
• It determines:
  • The axes (orientation and scaling)
  • The shape of geometric objects
  • The relationships between x and y aesthetics

Importance of coordinate systems

• They control how data is drawn, not what data is shown.
• Changing coordinates can:
  • Flip, stretch, or transform plots
  • Reveal patterns not visible in the default Cartesian system
  • Support specialized visualizations (like polar plots)

Common Coordinate Systems

RUG plot app

Create facets, violin plots, tikz using:

The Grammar o Graphics in python

plotnine